Python_EX-004. 10 way data read from a file

 

No.	Method	Code Example	Pros	Cons
1	read()	# to read the entire file with open('filename.txt', 'r') as file:     data = file.read()     print(data)	- Simple  - Easy to use	- Not suitable for large files (memory-intensive)
2	readline()	# to read one line at a time with open('filename.txt', 'r') as file:     line = file.readline()     while line:         print(line.strip())         line = file.readline()	- Suitable for large files - Allows processing   line-by-line	- Slightly complex - Requires handling   end-of-file manually
3	readlines()	#  to read all lines into a list with open('filename.txt', 'r') as file:     lines = file.readlines()     for line in lines:         print(line.strip())	- Easy to use - Stores all lines in a list	- Not suitable for large files (memory-intensive)
4	for loop	# to read line by line with open('filename.txt', 'r') as file:     for line in file:         print(line.strip())	- Simple & clean code - Suitable for large files	- Slightly less intuitive for beginners
5	seek()	# to read specific parts with open('filename.txt', 'r') as file:     file.seek(10)     data = file.read(20)     print(data)	- Allows random access to specific parts of the file - Useful for binary files	- Requires knowledge of file structure - More complex code
6	Reading a file	# reading a file # as a list comprehension with open('filename.txt', 'r') as file:     lines = [line.strip() for line in file]     print(lines)	- Concise & expressive code - Combines reading & processing	- Not suitable for large files (memory-intensive)
7	Path from pathlib	# Path from pathlib from pathlib import Path data = Path('filename.txt').read_text() print(data)	- Modern & user-friendly API - Integrates well with other pathlib functionalities	- Requires pathlib  (Python 3.4+) - Limited to text files
8	numpy	# to read data # (for structured data) import numpy as np data = np.loadtxt('filename.txt') print(data)	- Efficient for numerical data - Provides powerful data   manipulation capabilities	- Requires numpy library - Suitable mainly   for numerical data
9	pandas:	# to read data # (for tabular data) pimport pandas as pd df = pd.read_csv('filename.txt') print(df)	- Excellent for tabular data - Provides powerful  data analysis tools	Requires pandas library - More complex  than basic file reading
10	csv module	# to read CSV files import csv with open('filename.txt', 'r') as file:     reader = csv.reader(file)     for row in reader:         print(row)	- Built-in module (no additional installation required) - Suitable for CSV files	- Limited to CSV files - Requires handling of CSV-specific issues (e.g., delimiters, quoting)